Heval Ataş

KG-Orchestra enriches biomedical knowledge graphs by combining manual curation and automated LLM methods to improve granularity and evidence quality.

ECLIPSE predicts compound-protein interactions using heterogeneous biomedical knowledge graphs and graph neural networks, achieving strong performance on benchmark tasks.

The paper introduces CBM KG, a knowledge graph linking COVID-19 and neurodegenerative diseases through causal mechanisms, integrating 2,800 relationships from 63 studies.

This study evaluates computational methods for predicting drug-target interactions, finding that random data splits lead to over-optimism, learned embeddings perform well, and PCM models favor compound features.

DRUIDom predicts compound-target interactions using protein domain modularity, identifying 27,032 high-confidence associations and validating them experimentally on LIM kinases.

The paper introduces CROssBAR, a resource that integrates biomedical data and uses deep learning to predict entity relations, displaying results in knowledge graphs for systems-level analysis of diseases like COVID-19.

This study evaluates protein representation learning methods across three tasks: semantic similarity, function prediction, and drug target classification, highlighting their strengths and limitations.